Elements of Forecasting (with InfoTrac 1-Semester, Economic Applications Online Product, Data Sets Printed Access Card),   ISBN:9780324323597

Customer Rating: 

The book that i bought was really in a good shape. it was completely new even though i bought it with a price for old book. Reached me on time and was in the condition described by the sellerElements of Forecasting (with InfoTrac 1-Semester, Economic Applications Online Product, Data Sets Printed Access Card)

Customer Rating: 

The book starts with talking about forecasting deterministic trends, then seasonalities, later chapters 6,7,8 talk about forecasting cycles. Finally in the end chapters the author puts it all together and talks about multivariable forecasting models. The book is on an introductory level, so if you're looking for indepth discussion of these topics this is not for you. Anoter drawback is that this book does not integrate into its discussion of the topics any examples of code that would show how to forecast with any popular software package (Eviews or SAS).

Customer Rating: 

I posted the unfavorable review of the second edition. I have recently had an opportunity to see the third edition, and find the same errors are still present.

Customer Rating: 

There were a considerable number of errors in the first edition that I pointed out to the author shortly after its publication. The second edition seems to have corrected few if any of them. Let me cite two egregious examples.

In the chapter on ARMA models, the example analyzed is Canadian Employment data. One of the models that is fit is an MA(4) -- see pages 164-6. When I tried to reproduce these results using software other than EVIEWS, using the data disk in the 1st edition, I couldn't. I contacted EVIEWS and they discovered a programming error in the estimation routine. They released a patch to fix EVIEWS. However, the author never re-estimated his model, and the estimates in the second edition are the same as in the first. However, my copy of the 2nd edition has no data disk! Was that thought to be an adequate solution?!

Chapter 9 ("Putting it all together") is a capstone chapter that analyzes liquor sales data using the techniques introduced in earlier chapters. After several pages (pp. 207-19) a model is selected. On pages 220-2, the residuals are examined using the Box-Ljung statistic, and deemed acceptable. However, as a careful examination of table 9.6 makes clear, the p-values for the Box-Ljung statistic were computed as if the input data were a raw series. The model generating the residuals (p. 219) had 3 autoregressive terms! This changes the d.f. in the chi-square distribution of the statistic. If you make the appropriate correction using the data in table 9.6, and compute the p-values correctly, you will see that the model residuals apparently ARE NOT white noise. One reason is a calendar effect in liquor sales: months that contain more than a usual number of Fridays and Saturdays result in more liquor sales; ones with more Sundays result in lower liquor sales. However, the author doesn't discover this, but accepts his inappropriate model on the basis of faulty distribution theory.

Customer Rating: 

If the purpose of using this book is to get a brief idea of what certain concepts are then it is a good book. Unfortunately, many people using this book are going to be those who do not have much background with the concepts inside and they will be looking for clearer explanations of what the author is talking about. I think that is the book's weakness: the fact that many times I didn't feel that his definitions and explanations were complete enough.